Artificial Intelligence and Machine Learning

David Haus, PhD, Dean
Phone: (419) 995-8422
Email: haus.d@rhodesstate.edu
Office: JJC 117

The Associate in Science (AS) in Artificial Intelligence and Machine Learning Major focuses on building machine learning models that can be used for predicting, making decisions, and enhancing human capabilities. The program prepares students for entry-level positions in a variety of fields using artificial intelligence, including information technology, automotive, healthcare, aerospace, industrial, and manufacturing industries. Program content includes an introduction to artificial intelligence and machine learning, natural language processing, computer vision, and artificial intelligence for business solutions and other applications. The curriculum also includes coursework in computer programming, math, engineering, and statistics.

Program Learning Outcomes

Upon completion, the student will be able to:

Apply common artificial intelligence (AI) concepts and methodologies, including neural networks/deep learning, machine learning, natural language processing, computer vision, and data science, for analysis and decision making.
Apply artificial intelligence (AI) project development and machine learning life cycle to address social and business issues, opportunities, and problems.
Apply statistical analysis and machine learning algorithms to predict usefulness of artificial intelligence (AI) programming solutions.
Use appropriate programming languages to implement artificial intelligence (AI) solutions.
Communicate in varied settings, orally and visually and in writing, in a culturally responsive manner.
Collaborate with diverse individuals and teams to design and implement artificial intelligence (AI) and machine learning solutions.
Evaluate issues of bias, culture, environment, ethics, regulations, and professional expectations in the field of artificial intelligence (AI) and machine learning.

Technical Standards
See here for details.

First Year
Pre-requisite Semester		Hours
COM 1110	English Composition	3
CPT 1050	Technology Basics for IT Pro	3
MTH 1260	Statistics	3
PSY 1010	General Psychology	3
SDE 1010	First Year Experience	1
	Term Hours	13
Fall
AIM 1000	Introduction to Artificial Intelligence	3
HST 1610	American History to 1877	3
MTH 1711	Calculus I	5
POL 1010	Introduction to Political Science	3
	Term Hours	14
Spring
COM 1140	Technical Writing	3
CPT 1110	Introduction to Programming Logic and Design	3
CPT 2350	Database Programming	3
MTH 1721	Calculus II	5
	Term Hours	14
Second Year
Fall
AIM 1100	Introduction to Machine Learning	3
AIM 2991	AIM Field Experience	1
LIT 2210 or LIT 2215	Introduction to Literature or Native American Literature	3
PHY 1120	Physics I	4
	Term Hours	11
Spring
AIM 2200	Natural Language Processing	3
AIM 2220	Artificial Intelligence for Computer Vision	3
AIM 2970 	AIM Capstone	2
PHY 1130	Physics II	4
	Term Hours	12
	Total Hours	64

AIM 1000 — Introduction to Artificial Intelligence

Credit Hours: 3.00 Total Contact Hours: 4.00 Lecture Hours: 2.00 Lab Hours: 2.00

Introduces basic concepts and applications of artificial intelligence (AI), including AI project cycles. Focus on issues surrounding AI including ethics, bias, culture, regulations, and professional expectations.

AIM 1010 — Maths for AI

Credit Hours: 3.00 Total Contact Hours: 3.00 Lecture Hours: 3.00

Discusses basic concepts and topics relevant to understanding mathematics' role in AI. This course will utilize statistics, linear algebra, probability, and basic calculus. The content correlates each of these topics with reference to their role in the AI Project Cycle.

AIM 1100 — Introduction to Machine Learning

Credit Hours: 3.00 Total Contact Hours: 4.00 Lecture Hours: 2.00 Lab Hours: 2.00

Introduces machine learning concepts and Python applications, including data acquisition, supervised and unsupervised learning, and data modeling.
Prerequisites: AIM 1000, CPT 2350
Corequisites: MTH 1260.

AIM 2200 — Natural Language Processing

Credit Hours: 3.00 Total Contact Hours: 4.00 Lecture Hours: 2.00 Lab Hours: 2.00

Introduces the fundamental concepts in Natural Language Processing (NLP) and text processing. Focus on knowledge and skills necessary to create a language recognition application.
Prerequisites: AIM 1100
Corequisites: AIM 2220, AIM 2970.

AIM 2220 — Artificial Intelligence for Computer Vision

Credit Hours: 3.00 Total Contact Hours: 4.00 Lecture Hours: 2.00 Lab Hours: 2.00

Understands and applies the basic techniques to process images using OpenCV and Python libraries. Focuses on knowledge & skills necessary to apply AI in CV for common tasks like Image Classification and Object Detection.
Prerequisites: AIM 1100
Corequisites: AIM 2200, AIM 2970.

AIM 2970 — AIM Capstone 

Credit Hours: 2.00 Total Contact Hours: 4.00 Lab Hours: 4.00

Focuses on how a social issue is explored, brought through the Artificial Intelligence (AI) Project cycle, and delivered as a solution using the different domains of AI, including computer vision and natural language processing.
Prerequisites: AIM 1000, AIM 1100, COM 1110
Corequisites: AIM 2200, AIM 2220.

AIM 2991 — AIM Field Experience

Credit Hour: 1.00 Total Contact Hour: 1.00 Lecture Hour: 1.00

Enables work activity which relates to an individual student's occupational objectives. With permission of a faculty advisor, the field experience replaces elective or required courses in a student's associate degree program. The experience is coordinated by a faculty member of the college who assist the student in planning the experience, visits the site of the experience for a conference with the student and his/her supervisor at least once during the semester and assigns the course grade to the student after appropriate consultation with the employer/supervisor.
Prerequisites: AIM 1000 and faculty advisor approval.